Hybrid Intrusion Detection: Clustering-Outlier and Incremental SVM - Couverture souple

Chitrakar, Roshan; Huang, Chuanhe

 
9783659979217: Hybrid Intrusion Detection: Clustering-Outlier and Incremental SVM
Couverture souple
ISBN 10 : 365997921X ISBN 13 : 9783659979217
Editeur : LAP LAMBERT Academic Publishing, 2016

Synopsis

Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Newer intrusions are coming out every day with the all-way growth of the Internet. In this context, this book proposes a hybrid approach of intrusion detection along with architecture. The proposed architecture is flexible enough to carry intrusion detection tasks either by using a single module or by using multiple modules. Two modules - (1) Clustering-Outlier detection followed by SVM classification and (2) Incremental SVM with Half-partition method, are proposed in the book. Firstly, this work develops the “Clustering-Outlier Detection" algorithm that combines k-Medoids clustering and Outlier analysis. Secondly, this book introduces the Half-partition strategy and also designs “Candidate Support Vector Selection” algorithm for incremental SVM. This book is intended for the people who are working in the field of Intrusion Detection and Data Mining. Researchers and Scholars who are interested in k-Means and k-Medoids clustering and SVM classification in particular, will find this book useful. Students who want to pursue their research work in the fields of Information Security and Data Mining may also consider this as a good reference.

Biographie de l'auteur

Roshan Chitrakar, born in 1967, is a Ph.D. in Information Security of Wuhan University, China. His areas of interests are Data Mining, Programming Languages, Software Engineering, Databases etc. Starting IT career in 1987 at National Computer Centre, Nepal, he now works as an Associate Professor at Nepal College of Information Technology, Nepal.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2016
Langue
anglais
ISBN 10 
365997921X
ISBN 13 
9783659979217
Reliure
Broché
Nombre de pages
140
Coordonnées du fabricant
non disponible
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